# create by fanfan on 2020/3/27 0027
import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten
from keras.optimizers import SGD
from keras.layers import Conv2D,MaxPool2D

# 生成虚拟数据
x_train = np.random.random((100,100,100,3))
y_train = keras.utils.to_categorical(np.random.randint(10,size=(100,1)),num_classes=10)
x_test = np.random.random((20,100,100,3))
y_test = keras.utils.to_categorical(np.random.randint(10,size=(20,1)),num_classes=10)

model = Sequential()
# 输入: 3 通道 100x100 像素图像 -> (100, 100, 3) 张量。
# 使用 32 个大小为 3x3 的卷积滤波器。
model.add(Conv2D(32,(3,3),activation='relu',input_shape=(100,100,3)))
model.add(Conv2D(32,(3,3),activation='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(64,(3,3),activation='relu'))
model.add(Conv2D(64,(3,3),activation='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(256,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10,activation='softmax'))

sgd = SGD(lr=0.01,decay=1e-6,momentum=0.9,nesterov=True)
model.compile(loss="categorical_crossentropy",optimizer=sgd)

model.fit(x_train,y_train,batch_size=32,epochs=10)
score = model.evaluate(x_test,y_test,batch_size=32)

